1) Handwritten Chinese Character Recognition
识别手写汉字
1.
Fundamental Units Fuzzy Relations Approach for Handwritten Chinese Character Recognition;
识别手写汉字的基元模糊关系法
2) handwritten Chinese character recognition
手写汉字识别
1.
In handwritten Chinese character recognition,many misclassification errors come from the confusion of similar characters.
为解决手写汉字识别中的相似字混淆问题,提出了一种基于镜像学习和复合二次距离的识别算法,提高现有的二次分类器对相似汉字的鉴别能力。
2.
The paper describes a method of handwritten Chinese character recognition on-line which can be used in the management of front-end processors and broadcasting & television lab as well as measurement & control system which only need a few Chinese characters.
本文针对其管理和测控系统操作只需少量汉字服务的实际情况,提出一种在线手写汉字识别用于测控系统操作的方法。
3.
On analysis of online and offline handwritten Chinese character recognition system,a video-based Online Handwritten Chinese Character Recognition Method is proposed.
在分析传统联机与脱机手写汉字识别系统基础上 ,本文提出了一种基于运动图像的在线手写汉字识别新方法 。
3) handwritten Chinese character recognition
手写体汉字识别
1.
Small Set Handwritten Chinese Character Recognition Based on Integration RBF Neural Network;
基于集成RBF神经网络的小类别手写体汉字识别系统
2.
By choosing stroke and substroke as basic components to analyse the composition regularities and deformation regularities of handwritten Chinese character, two models are presented for representing the structure for handwritten Chinese character recognition: the substroke center model and the stroke relation matrix model, as the classifying criteria and methods based on models.
针对手写体汉字识别问题,选取笔段和笔划作为基元,分析手写体汉字的组成规律和变形规律,提出了两种汉字结构模型:笔段中心点模型和笔划关系矩阵模型,以及基于模型的分类依据和识别方法。
3.
According to the feature extraction of the handwritten Chinese character recognition, an improved extracting stroke plane method was proposed.
针对手写体汉字识别过程中的特征抽取,提出了一种改进的抽取笔画平面的方法。
4) handwritten Chinese characters recognition
手写体汉字识别
1.
A system of handwritten Chinese characters recognition based on feedback structure is construc- ted.
构建一种基于反馈结构的手写体汉字识别系统。
5) HCCR
汉字手写体识别
6) off-line handwritten Chinese character recognition
脱机手写体汉字识别
1.
Algorithms Research on Thinning, Feature Extracting and Similar Chinese Characters Recognition for Off-line Handwritten Chinese Character Recognition;
脱机手写体汉字识别中细化、特征提取和相似字识别算法研究
2.
The proposed off-line handwritten Chinese character recognition system was composed of a feature extraction module and a recognition module.
提出的脱机手写体汉字识别系统主要研究特征提取和分类识别两个模块。
3.
This paper researches on feature extraction methods for off-line handwritten Chinese character recognition applied to automatic recognition of financial bills.
针对金融票据自动识别应用中的脱机手写体汉字识别进行特征提取的研究,首先提出了用Gabor特征和Zernike矩特征来分别表征汉字的局部特征和全局特征。
补充资料：汉字分类识别
汉字分类识别
Chinese character recognition by classification
hQnzi fsnlei ShibiG汉字分类识别(Chi~ch~ter八叉邺，itionby cl别洛ifiCation】根据某种判别准则，用统计或结构的方法，把汉字模式多维特征向量(参见汉宇识别鑫本方法)构成的特征空间划分为若干子空间的过程。 由于汉字数量巨大，汉字模式样本甚多，为提高识别速度，汉字识别通常采用两到三级分类识别方案。其中最后一级分类(通过该分类就识别出汉字)称为细分类，前面若干级分类都称为粗分类。 汉字识别分类方法的选择原则是:①分类特性好。即类内各样本距离小，类间距离大，各类重叠样本少。②分类稳定性好，抗干扰能力强。③粗分类的正确分类率和稳定性应比细分类高。④每类汉字的相关性要小，分类速度快。⑤分类特征易提取，简单，维数低。⑥粗分类要和细分类相协调。以上要求很难同时满足，分类(特别是粗分类)的稳定性和正确分类率是首先需要考虑的。 分类方法有重叠区分类、中心提取分类、判定树分类、引导树分类等。 在汉字分类时，用某种类似度(或距离)准则，把未知文字特征和字典中标准文字特征逐一比较，按相似性从大到小排序，取i位以前(包括第i位)的所有文字为一类，所得到的正确分类的文字占全部需要识别文字的百分比，称为累计分类率。第i位的累计分类率就是从第1位到第i位所得正确分类字数占全部需要识别文字的百分比。第1位的累计分类率就是识别率，第。位(n为全部需要识别文字数)累计分类率总是100%。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条